code: def test_in_memory_finetune(self):
      SST_FILENAME = "SST-binary.csv"
      DATA_PATH = os.path.join("Data", "Classify", SST_FILENAME)
      dataframe = pd.read_csv(DATA_PATH, nrows=100).dropna()
      trainX, testX, trainY, testY = train_test_split(
          list(dataframe.Text.values),
          list(dataframe.Target.values),
          test_size=0.3,
          random_state=42,
      )
      in_memory_finetune = [
          {
              "config": {"n_epochs": 1, "max_length": 64},
              "X": trainX,
              "Y": trainY,
              "X_test": testX,
              "Y_test": testY,
              "name": "sst-b",
              "every_n_iter": 30,
          }
      ]
      model = Classifier(
          in_memory_finetune=in_memory_finetune, max_length=64
      )
      model.fit(trainX, trainY)
      metrics = read_eval_metrics(
          os.path.join(model.estimator_dir, "finetuning")
      )
      for step, metric in metrics.items():
          self.assertEqual(len(metric), 2)  # train and test
          for key, value in metric.items():
              self.assertGreaterEqual(value, 0)
              self.assertLessEqual(value, 1)
              self.assertIn("finetuning", key)
  
file_path: finetune/tests/test_oscar_specific.py
imports: import unittest
  import os
  import pandas
  from sklearn.model_selection import train_test_split
  from finetune.base_models import OSCAR
  from finetune import Classifier
  from finetune.util.metrics import read_eval_metrics
label_desc: Use image node_id 1 for calling the TestOscarFeatures obj's underlying member method code with example usage: obj.test_in_memory_finetune() without return types